Computer architecture for training a correlithm object processing system

ABSTRACT

A correlithm object processing system that includes a trainer configured to send a node entry request to a node engine that triggers the node engine to generate an entry in a node table. The trainer is further configured to receive a source correlithm object and a target correlithm object in response to sending the node entry request. The trainer is further configured to send a real world input value and the source correlithm object to a sensor engine which triggers the sensor engine to generate an entry in a sensor table linking the real world input value and the source correlithm object. The trainer is further configured to send a real world output value and the target correlithm object to an actor engine which triggers the actor engine to generate an entry in an actor table linking the real world output value and the target correlithm object.

TECHNICAL FIELD

The present disclosure relates generally to computer architectures foremulating a processing system, and more specifically to computerarchitectures for emulating a correlithm object processing system.

BACKGROUND

Conventional computers are highly attuned to using operations thatrequire manipulating ordinal numbers, especially ordinal binaryintegers. The value of an ordinal number corresponds with its positionin a set of sequentially ordered number values. These computers useordinal binary integers to represent, manipulate, and store information.These computers rely on the numerical order of ordinal binary integersrepresenting data to perform various operations such as counting,sorting, indexing, and mathematical calculations. Even when performingoperations that involve other number systems (e.g. floating point),conventional computers still resort to using ordinal binary integers toperform any operations.

Ordinal based number systems only provide information about the sequenceorder of the numbers themselves based on their numeric values. Ordinalnumbers do not provide any information about any other types ofrelationships for the data being represented by the numeric values suchas similarity. For example, when a conventional computer uses ordinalnumbers to represent data samples (e.g. images or audio signals),different data samples are represented by different numeric values. Thedifferent numeric values do not provide any information about howsimilar or dissimilar one data sample is from another. Unless there isan exact match in ordinal number values, conventional systems are unableto tell if a data sample matches or is similar to any other datasamples. As a result, conventional computers are unable to use ordinalnumbers by themselves for comparing different data samples and insteadthese computers rely on complex signal processing techniques.Determining whether a data sample matches or is similar to other datasamples is not a trivial task and poses several technical challenges forconventional computers. These technical challenges result in complexprocesses that consume processing power which reduces the speed andperformance of the system. The ability to compare unknown data samplesto known data samples is crucial for many security applications such asface recognition, voice recognition, and fraud detection.

Thus, it is desirable to provide a solution that allows computingsystems to efficiently determine how similar different data samples areto each other and to perform operations based on their similarity.

SUMMARY

Conventional computers are highly attuned to using operations thatrequire manipulating ordinal numbers, especially ordinal binaryintegers. The value of an ordinal number corresponds with its positionin a set of sequentially ordered number values. These computers useordinal binary integers to represent, manipulate, and store information.These computers rely on the numerical order of ordinal binary integersrepresenting data to perform various operations such as counting,sorting, indexing, and mathematical calculations. Even when performingoperations that involve other number systems (e.g. floating point),conventional computers still resort to using ordinal binary integers toperform any operations.

Ordinal based number systems only provide information about the sequenceorder of the numbers themselves based on their numeric values. Ordinalnumbers do not provide any information about any other types ofrelationships for the data being represented by the numeric values suchas similarity. For example, when a conventional computer uses ordinalnumbers to represent data samples (e.g. images or audio signals),different data samples are represented by different numeric values. Thedifferent numeric values do not provide any information about howsimilar or dissimilar one data sample is from another. Unless there isan exact match in ordinal number values, conventional systems are unableto tell if a data sample matches or is similar to any other datasamples. As a result, conventional computers are unable to use ordinalnumbers by themselves for comparing different data samples and insteadthese computers rely on complex signal processing techniques.Determining whether a data sample matches or is similar to other datasamples is not a trivial task and poses several technical challenges forconventional computers. These technical challenges result in complexprocesses that consume processing power which reduces the speed andperformance of the system. The ability to compare unknown data samplesto known data samples is crucial for many applications such as securityapplication (e.g. face recognition, voice recognition, and frauddetection).

The system described in the present application provides a technicalsolution that enables the system to efficiently determine how similardifferent objects are to each other and to perform operations based ontheir similarity. In contrast to conventional systems, the system usesan unconventional configuration to perform various operations usingcategorical numbers and geometric objects, also referred to ascorrelithm objects, instead of ordinal numbers. Using categoricalnumbers and correlithm objects on a conventional device involveschanging the traditional operation of the computer to supportrepresenting and manipulating concepts as correlithm objects. A deviceor system may be configured to implement or emulate a special purposecomputing device capable of performing operations using correlithmobjects. Implementing or emulating a correlithm object processing systemimproves the operation of a device by enabling the device to performnon-binary comparisons (i.e. match or no match) between different datasamples. This enables the device to quantify a degree of similaritybetween different data samples. This increases the flexibility of thedevice to work with data samples having different data types and/orformats, and also increases the speed and performance of the device whenperforming operations using data samples. These technical advantages andother improvements to the device are described in more detail throughoutthe disclosure.

In one embodiment, the system is configured to use binary integers ascategorical numbers rather than ordinal numbers which enables the systemto determine how similar a data sample is to other data samples.Categorical numbers provide information about similar or dissimilardifferent data samples are from each other. For example, categoricalnumbers can be used in facial recognition applications to representdifferent images of faces and/or features of the faces. The systemprovides a technical advantage by allowing the system to assigncorrelithm objects represented by categorical numbers to different datasamples based on how similar they are to other data samples. As anexample, the system is able to assign correlithm objects to differentimages of people such that the correlithm objects can be directly usedto determine how similar the people in the images are to each other. Inother words, the system is able to use correlithm objects in facialrecognition applications to quickly determine whether a captured imageof a person matches any previously stored images without relying onconventional signal processing techniques.

Correlithm object processing systems use new types of data structurescalled correlithm objects that improve the way a device operates, forexample, by enabling the device to perform non-binary data setcomparisons and to quantify the similarity between different datasamples. Correlithm objects are data structures designed to improve theway a device stores, retrieves, and compares data samples in memory.Correlithm objects also provide a data structure that is independent ofthe data type and format of the data samples they represent. Correlithmobjects allow data samples to be directly compared regardless of theiroriginal data type and/or format.

A correlithm object processing system uses a combination of a sensortable, a node table, and/or an actor table to provide a specific set ofrules that improve computer-related technologies by enabling devices tocompare and to determine the degree of similarity between different datasamples regardless of the data type and/or format of the data samplethey represent. The ability to directly compare data samples havingdifferent data types and/or formatting is a new functionality thatcannot be performed using conventional computing systems and datastructures.

In addition, correlithm object processing system uses a combination of asensor table, a node table, and/or an actor table to provide aparticular manner for transforming data samples between ordinal numberrepresentations and correlithm objects in a correlithm object domain.Transforming data samples between ordinal number representations andcorrelithm objects involves fundamentally changing the data type of datasamples between an ordinal number system and a categorical number systemto achieve the previously described benefits of the correlithm objectprocessing system.

Using correlithm objects allows the system or device to compare datasamples (e.g. images) even when the input data sample does not exactlymatch any known or previously stored input values. For example, an inputdata sample that is an image may have different lighting conditions thanthe previously stored images. The differences in lighting conditions canmake images of the same person appear different from each other. Thedevice uses an unconventional configuration that implements a correlithmobject processing system that uses the distance between the data sampleswhich are represented as correlithm objects and other known data samplesto determine whether the input data sample matches or is similar to theother known data samples. Implementing a correlithm object processingsystem fundamentally changes the device and the traditional dataprocessing paradigm. Implementing the correlithm object processingsystem improves the operation of the device by enabling the device toperform non-binary comparisons of data samples. In other words, thedevice is able to determine how similar the data samples are to eachother even when the data samples are not exact matches. In addition, thedevice is able to quantify how similar data samples are to one another.The ability to determine how similar data samples are to each other isunique and distinct from conventional computers that can only performbinary comparisons to identify exact matches.

The problems associated with comparing data sets and identifying matchesbased on the comparison are problems necessarily rooted in computertechnologies. As described above, conventional systems are limited to abinary comparison that can only determine whether an exact match isfound. Emulating a correlithm object processing system provides atechnical solution that addresses problems associated with comparingdata sets and identifying matches. Using correlithm objects to representdata samples fundamentally changes the operation of a device and how thedevice views data samples. By implementing a correlithm objectprocessing system, the device can determine the distance between thedata samples and other known data samples to determine whether the inputdata sample matches or is similar to the other known data samples. Inaddition, the device is able to determine a degree of similarity thatquantifies how similar different data samples are to one another.

In one embodiment, the system is configured to use a trainer to extendthe functionality of a device emulating a correlithm object processingsystem. Correlithm object processing systems typically use a combinationof sensors, nodes, and actors to perform operations. Each of thesecomponents uses its respective table to transform data between realworld values and correlithm objects. One technical problem occurs when auser wants to extend the capabilities of a correlithm object processingsystems by introducing a new input and/or output. Configuring acorrelithm object processing system to support a new input or outputinvolves updating and modifying affected components. This processinvolves synchronizing and exchanging information across multiplecomponents. However, individual components lack the ability tocoordinate with other components to exchange information to properlyupdate their tables. A trainer provides a technical solution that trainssensors, nodes, and actors to support new inputs and outputs by creatingnew entries in their corresponding tables. The trainer facilitatessynchronizing and exchanging information among the components in acorrelithm object processing system to ensure each component has theinformation it needs to update its table. By modifying sensor tables,node tables, and/or actor tables, the trainer increases thefunctionality of a device by expanding the capabilities of itscorrelithm object processing system.

Certain embodiments of the present disclosure may include some, all, ornone of these advantages. These advantages and other features will bemore clearly understood from the following detailed description taken inconjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following brief description, taken in connection with theaccompanying drawings and detailed description, wherein like referencenumerals represent like parts.

FIG. 1 is a schematic view of an embodiment of a special purposecomputer implementing correlithm objects in an n-dimensional space;

FIG. 2 is a perspective view of an embodiment of a mapping betweencorrelithm objects in different n-dimensional spaces;

FIG. 3 is a schematic view of an embodiment of a correlithm objectprocessing system;

FIG. 4 is a protocol diagram of an embodiment of a correlithm objectprocess flow;

FIG. 5 is a schematic diagram of an embodiment a computer architecturefor emulating a correlithm object processing system;

FIG. 6 is a schematic diagram of an embodiment for training a correlithmobject processing system;

FIG. 7 is a protocol diagram of an embodiment of a correlithm objectprocessing system training flow; and

FIG. 8 is a protocol diagram of another embodiment of a correlithmobject processing system training flow.

DETAILED DESCRIPTION

FIGS. 1-5 generally describe various embodiments of how a correlithmobject processing system may be implemented or emulated in hardware,such as a special purpose computer. FIG. 1 is a schematic view of anembodiment of a user device 100 implementing correlithm objects 104 inan n-dimensional space 102. Examples of user devices 100 include, butare not limited to, desktop computers, mobile phones, tablet computers,laptop computers, or other special purpose computer platform. The userdevice 100 is configured to implement or emulate a correlithm objectprocessing system that uses categorical numbers to represent datasamples as correlithm objects 104 in a high-dimensional space 102, forexample a high-dimensional binary cube. Additional information about thecorrelithm object processing system is described in FIG. 3. Additionalinformation about configuring the user device 100 to implement oremulate a correlithm object processing system is described in FIG. 5.

Conventional computers rely on the numerical order of ordinal binaryintegers representing data to perform various operations such ascounting, sorting, indexing, and mathematical calculations. Even whenperforming operations that involve other number systems (e.g. floatingpoint), conventional computers still resort to using ordinal binaryintegers to perform any operations. Ordinal based number systems onlyprovide information about the sequence order of the numbers themselvesbased on their numeric values. Ordinal numbers do not provide anyinformation about any other types of relationships for the data beingrepresented by the numeric values, such as similarity. For example, whena conventional computer uses ordinal numbers to represent data samples(e.g. images or audio signals), different data samples are representedby different numeric values. The different numeric values do not provideany information about how similar or dissimilar one data sample is fromanother. In other words, conventional computers are only able to makebinary comparisons of data samples which only results in determiningwhether the data samples match or do not match. Unless there is an exactmatch in ordinal number values, conventional systems are unable to tellif a data sample matches or is similar to any other data samples. As aresult, conventional computers are unable to use ordinal numbers bythemselves for determining similarity between different data samples,and instead these computers rely on complex signal processingtechniques. Determining whether a data sample matches or is similar toother data samples is not a trivial task and poses several technicalchallenges for conventional computers. These technical challenges resultin complex processes that consume processing power which reduces thespeed and performance of the system.

In contrast to conventional systems, the user device 100 operates as aspecial purpose machine for implementing or emulating a correlithmobject processing system. Implementing or emulating a correlithm objectprocessing system improves the operation of the user device 100 byenabling the user device 100 to perform non-binary comparisons (i.e.match or no match) between different data samples. This enables the userdevice 100 to quantify a degree of similarity between different datasamples. This increases the flexibility of the user device 100 to workwith data samples having different data types and/or formats, and alsoincreases the speed and performance of the user device 100 whenperforming operations using data samples. These improvements and otherbenefits to the user device 100 are described in more detail below andthroughout the disclosure.

For example, the user device 100 employs the correlithm objectprocessing system to allow the user device 100 to compare data sampleseven when the input data sample does not exactly match any known orpreviously stored input values. Implementing a correlithm objectprocessing system fundamentally changes the user device 100 and thetraditional data processing paradigm. Implementing the correlithm objectprocessing system improves the operation of the user device 100 byenabling the user device 100 to perform non-binary comparisons of datasamples. In other words, the user device 100 is able to determine howsimilar the data samples are to each other even when the data samplesare not exact matches. In addition, the user device 100 is able toquantify how similar data samples are to one another. The ability todetermine how similar data samples are to each others is unique anddistinct from conventional computers that can only perform binarycomparisons to identify exact matches.

The user device's 100 ability to perform non-binary comparisons of datasamples also fundamentally changes traditional data searching paradigms.For example, conventional search engines rely on finding exact matchesor exact partial matches of search tokens to identify related datasamples. For instance, conventional text-based search engine are limitedto finding related data samples that have text that exactly matchesother data samples. These search engines only provide a binary resultthat identifies whether or not an exact match was found based on thesearch token. Implementing the correlithm object processing systemimproves the operation of the user device 100 by enabling the userdevice 100 to identify related data samples based on how similar thesearch token is to other data sample. These improvements result inincreased flexibility and faster search time when using a correlithmobject processing system. The ability to identify similarities betweendata samples expands the capabilities of a search engine to include datasamples that may not have an exact match with a search token but arestill related and similar in some aspects. The user device 100 is alsoable to quantify how similar data samples are to each other based oncharacteristics besides exact matches to the search token. Implementingthe correlithm object processing system involves operating the userdevice 100 in an unconventional manner to achieve these technologicalimprovements as well as other benefits described below for the userdevice 100.

Computing devices typically rely on the ability to compare data sets(e.g. data samples) to one another for processing. For example, insecurity or authentication applications a computing device is configuredto compare an input of an unknown person to a data set of known people(or biometric information associated with these people). The problemsassociated with comparing data sets and identifying matches based on thecomparison are problems necessarily rooted in computer technologies. Asdescribed above, conventional systems are limited to a binary comparisonthat can only determine whether an exact match is found. As an example,an input data sample that is an image of a person may have differentlighting conditions than previously stored images. In this example,different lighting conditions can make images of the same person appeardifferent from each other. Conventional computers are unable todistinguish between two images of the same person with differentlighting conditions and two images of two different people withoutcomplicated signal processing. In both of these cases, conventionalcomputers can only determine that the images are different. This isbecause conventional computers rely on manipulating ordinal numbers forprocessing.

In contrast, the user device 100 uses an unconventional configurationthat uses correlithm objects to represent data samples. Using correlithmobjects to represent data samples fundamentally changes the operation ofthe user device 100 and how the device views data samples. Byimplementing a correlithm object processing system, the user device 100can determine the distance between the data samples and other known datasamples to determine whether the input data sample matches or is similarto the other known data samples, as explained in detail below. Unlikethe conventional computers described in the previous example, the userdevice 100 is able to distinguish between two images of the same personwith different lighting conditions and two images of two differentpeople by using correlithm objects 104. Correlithm objects allow theuser device 100 to determine whether there are any similarities betweendata samples, such as between two images that are different from eachother in some respects but similar in other respects. For example, theuser device 100 is able to determine that despite different lightingconditions, the same person is present in both images.

In addition, the user device 100 is able to determine a degree ofsimilarity that quantifies how similar different data samples are to oneanother. Implementing a correlithm object processing system in the userdevice 100 improves the operation of the user device 100 when comparingdata sets and identifying matches by allowing the user device 100 toperform non-binary comparisons between data sets and to quantify thesimilarity between different data samples. In addition, using acorrelithm object processing system results in increased flexibility andfaster search times when comparing data samples or data sets. Thus,implementing a correlithm object processing system in the user device100 provides a technical solution to a problem necessarily rooted incomputer technologies.

The ability to implement a correlithm object processing system providesa technical advantage by allowing the system to identify and comparedata samples regardless of whether an exact match has been previousobserved or stored. In other words, using the correlithm objectprocessing system the user device 100 is able to identify similar datasamples to an input data sample in the absence of an exact match. Thisfunctionality is unique and distinct from conventional computers thatcan only identify data samples with exact matches.

Examples of data samples include, but are not limited to, images, files,text, audio signals, biometric signals, electric signals, or any othersuitable type of data. A correlithm object 104 is a point in then-dimensional space 102, sometimes called an “n-space.” The value ofrepresents the number of dimensions of the space. For example, ann-dimensional space 102 may be a 3-dimensional space, a 50-dimensionalspace, a 100-dimensional space, or any other suitable dimension space.The number of dimensions depends on its ability to support certainstatistical tests, such as the distances between pairs of randomlychosen points in the space approximating a normal distribution. In someembodiments, increasing the number of dimensions in the n-dimensionalspace 102 modifies the statistical properties of the system to provideimproved results. Increasing the number of dimensions increases theprobability that a correlithm object 104 is similar to other adjacentcorrelithm objects 104. In other words, increasing the number ofdimensions increases the correlation between how close a pair ofcorrelithm objects 104 are to each other and how similar the correlithmobjects 104 are to each other.

Correlithm object processing systems use new types of data structurescalled correlithm objects 104 that improve the way a device operates,for example, by enabling the device to perform non-binary data setcomparisons and to quantify the similarity between different datasamples. Correlithm objects 104 are data structures designed to improvethe way a device stores, retrieves, and compares data samples in memory.Unlike conventional data structures, correlithm objects 104 are datastructures where objects can be expressed in a high-dimensional spacesuch that distance 106 between points in the space represent thesimilarity between different objects or data samples. In other words,the distance 106 between a pair of correlithm objects 104 in then-dimensional space 102 indicates how similar the correlithm objects 104are from each other and the data samples they represent. Correlithmobjects 104 that are close to each other are more similar to each otherthan correlithm objects 104 that are further apart from each other. Forexample, in a facial recognition application, correlithm objects 104used to represent images of different types of glasses may be relativelyclose to each other compared to correlithm objects 104 used to representimages of other features such as facial hair. An exact match between twodata samples occurs when their corresponding correlithm objects 104 arethe same or have no distance between them. When two data samples are notexact matches but are similar, the distance between their correlithmobjects 104 can be used to indicate their similarities. In other words,the distance 106 between correlithm objects 104 can be used to identifyboth data samples that exactly match each other as well as data samplesthat do not match but are similar. This feature is unique to acorrelithm processing system and is unlike conventional computers thatare unable to detect when data samples are different but similar in someaspects.

Correlithm objects 104 also provide a data structure that is independentof the data type and format of the data samples they represent.Correlithm objects 104 allow data samples to be directly comparedregardless of their original data type and/or format. In some instances,comparing data samples as correlithm objects 104 is computationally moreefficient and faster than comparing data samples in their originalformat. For example, comparing images using conventional data structuresinvolves significant amounts of image processing which is time consumingand consumes processing resources. Thus, using correlithm objects 104 torepresent data samples provides increased flexibility and improvedperformance compared to using other conventional data structures.

In one embodiment, correlithm objects 104 may be represented usingcategorical binary strings. The number of bits used to represent thecorrelithm object 104 corresponds with the number of dimensions of then-dimensional space 102 where the correlithm object 102 is located. Forexample, each correlithm object 104 may be uniquely identified using a64-bit string in a 64-dimensional space 102. As another example, eachcorrelithm object 104 may be uniquely identified using a 10-bit stringin a 10-dimensional space 102. In other examples, correlithm objects 104can be identified using any other suitable number of bits in a stringthat corresponds with the number of dimensions in the n-dimensionalspace 102.

In this configuration, the distance 106 between two correlithm objects104 can be determined based on the differences between the bits of thetwo correlithm objects 104. In other words, the distance 106 between twocorrelithm objects can be determined based on how many individual bitsdiffer between the correlithm objects 104. The distance 106 between twocorrelithm objects 104 can be computed using Hamming distance or anyother suitable technique.

As an example using a 10-dimensional space 102, a first correlithmobject 104 is represented by a first 10-bit string (1001011011) and asecond correlithm object 104 is represented by a second 10-bit string(1000011011). The Hamming distance corresponds with the number of bitsthat differ between the first correlithm object 104 and the secondcorrelithm object 104. In other words, the Hamming distance between thefirst correlithm object 104 and the second correlithm object 104 can becomputed as follows:

$\quad\begin{matrix}1001011011 \\1000011011 \\{\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}} \\0001000000\end{matrix}$

In this example, the Hamming distance is equal to one because only onebit differs between the first correlithm object 104 and the secondcorrelithm object. As another example, a third correlithm object 104 isrepresented by a third 10-bit string (0110100100). In this example, theHamming distance between the first correlithm object 104 and the thirdcorrelithm object 104 can be computed as follows:

$\quad{\quad\begin{matrix}1001011011 \\0110100100 \\{\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}\text{-}} \\1111111111\end{matrix}}$

The Hamming distance is equal to ten because all of the bits aredifferent between the first correlithm object 104 and the thirdcorrelithm object 104. In the previous example, a Hamming distance equalto one indicates that the first correlithm object 104 and the secondcorrelithm object 104 are close to each other in the n-dimensional space102, which means they are similar to each other. In the second example,a Hamming distance equal to ten indicates that the first correlithmobject 104 and the third correlithm object 104 are further from eachother in the n-dimensional space 102 and are less similar to each otherthan the first correlithm object 104 and the second correlithm object104. In other words, the similarity between a pair of correlithm objectscan be readily determined based on the distance between the paircorrelithm objects.

As another example, the distance between a pair of correlithm objects104 can be determined by performing an XOR operation between the pair ofcorrelithm objects 104 and counting the number of logical high values inthe binary string. The number of logical high values indicates thenumber of bits that are different between the pair of correlithm objects104 which also corresponds with the Hamming distance between the pair ofcorrelithm objects 104.

In another embodiment, the distance 106 between two correlithm objects104 can be determined using a Minkowski distance such as the Euclideanor “straight-line” distance between the correlithm objects 104. Forexample, the distance 106 between a pair of correlithm objects 104 maybe determined by calculating the square root of the sum of squares ofthe coordinate difference in each dimension.

The user device 100 is configured to implement or emulate a correlithmobject processing system that comprises one or more sensors 302, nodes304, and/or actors 306 in order to convert data samples between realworld values or representations and to correlithm objects 104 in acorrelithm object domain. Sensors 302 are generally configured toconvert real world data samples to the correlithm object domain. Nodes304 are generally configured to process or perform various operations oncorrelithm objects in the correlithm object domain. Actors 306 aregenerally configured to convert correlithm objects 104 into real worldvalues or representations. Additional information about sensors 302,nodes 304, and actors 306 is described in FIG. 3.

Performing operations using correlithm objects 104 in a correlithmobject domain allows the user device 100 to identify relationshipsbetween data samples that cannot be identified using conventional dataprocessing systems. For example, in the correlithm object domain, theuser device 100 is able to identify not only data samples that exactlymatch an input data sample, but also other data samples that havesimilar characteristics or features as the input data samples.Conventional computers are unable to identify these types ofrelationships readily. Using correlithm objects 104 improves theoperation of the user device 100 by enabling the user device 100 toefficiently process data samples and identify relationships between datasamples without relying on signal processing techniques that require asignificant amount of processing resources. These benefits allow theuser device 100 to operate more efficiently than conventional computersby reducing the amount of processing power and resources that are neededto perform various operations.

FIG. 2 is a schematic view of an embodiment of a mapping betweencorrelithm objects 104 in different n-dimensional spaces 102. Whenimplementing a correlithm object processing system, the user device 100performs operations within the correlithm object domain using correlithmobjects 104 in different n-dimensional spaces 102. As an example, theuser device 100 may convert different types of data samples having realworld values into correlithm objects 104 in different n-dimensionalspaces 102. For instance, the user device 100 may convert data samplesof text into a first set of correlithm objects 104 in a firstn-dimensional space 102 and data samples of audio samples as a secondset of correlithm objects 104 in a second n-dimensional space 102.Conventional systems require data samples to be of the same type and/orformat in order to perform any kind of operation on the data samples. Insome instances, some types of data samples cannot be compared becausethere is no common format available. For example, conventional computersare unable to compare data samples of images and data samples of audiosamples because there is no common format. In contrast, the user device100 implementing a correlithm object processing system is able tocompare and perform operations using correlithm objects 104 in thecorrelithm object domain regardless of the type or format of theoriginal data samples.

In FIG. 2, a first set of correlithm objects 104A are defined within afirst n-dimensional space 102A and a second set of correlithm objects104B are defined within a second n-dimensional space 102B. Then-dimensional spaces may have the same number of dimensions or adifferent number of dimensions. For example, the first n-dimensionalspace 102A and the second n-dimensional space 102B may both be threedimensional spaces. As another example, the first n-dimensional space102A may be a three-dimensional space and the second n-dimensional space102B may be a nine dimensional space. Correlithm objects 104 in thefirst n-dimensional space 102A and second n-dimensional space 102B aremapped to each other. In other words, a correlithm object 104A in thefirst n-dimensional space 102A may reference or be linked with aparticular correlithm object 104B in the second n-dimensional space102B. The correlithm objects 104 may also be linked with and referencedwith other correlithm objects 104 in other n-dimensional spaces 102.

In one embodiment, a data structure such as table 200 may be used to mapor link correlithm objects 104 in different n-dimensional spaces 102. Insome instances, table 200 is referred to as a node table. Table 200 isgenerally configured to identify a first plurality of correlithm objects104 in a first n-dimensional space 102 and a second plurality ofcorrelithm objects 104 in a second n-dimensional space 102. Eachcorrelithm object 104 in the first n-dimensional space 102 is linkedwith a correlithm object 104 is the second n-dimensional space 102. Forexample, table 200 may be configured with a first column 202 that listscorrelithm objects 104A as source correlithm objects and a second column204 that lists corresponding correlithm objects 104B as targetcorrelithm objects. In other examples, table 200 may be configured inany other suitable manner or may be implemented using any other suitabledata structure. In some embodiments, one or more mapping functions maybe used to convert between a correlithm object 104 in a firstn-dimensional space and a correlithm object 104 is a secondn-dimensional space.

FIG. 3 is a schematic view of an embodiment of a correlithm objectprocessing system 300 that is implemented by a user device 100 toperform operations using correlithm objects 104. The system 300generally comprises a sensor 302, a node 304, and an actor 306. Thesystem 300 may be configured with any suitable number and/orconfiguration of sensors 302, nodes 304, and actors 306. An example ofthe system 300 in operation is described in FIG. 4. In one embodiment, asensor 302, a node 304, and an actor 306 may all be implemented on thesame device (e.g. user device 100). In other embodiments, a sensor 302,a node 304, and an actor 306 may each be implemented on differentdevices in signal communication with each other for example over anetwork. In other embodiments, different devices may be configured toimplement any combination of sensors 302, nodes 304, and actors 306.

Sensors 302 serve as interfaces that allow a user device 100 to convertreal world data samples into correlithm objects 104 that can be used inthe correlithm object domain. Sensors 302 enable the user device 100 tocompare and perform operations using correlithm objects 104 regardlessof the data type or format of the original data sample. Sensors 302 areconfigured to receive a real world value 320 representing a data sampleas an input, to determine a correlithm object 104 based on the realworld value 320, and to output the correlithm object 104. For example,the sensor 302 may receive an image 301 of a person and output acorrelithm object 322 to the node 304 or actor 306. In one embodiment,sensors 302 are configured to use sensor tables 308 that link aplurality of real world values with a plurality of correlithm objects104 in an n-dimensional space 102. Real world values are any type ofsignal, value, or representation of data samples. Examples of real worldvalues include, but are not limited to, images, pixel values, text,audio signals, electrical signals, and biometric signals. As an example,a sensor table 308 may be configured with a first column 312 that listsreal world value entries corresponding with different images and asecond column 314 that lists corresponding correlithm objects 104 asinput correlithm objects. In other examples, sensor tables 308 may beconfigured in any other suitable manner or may be implemented using anyother suitable data structure. In some embodiments, one or more mappingfunctions may be used to translate between a real world value 320 and acorrelithm object 104 in an n-dimensional space. Additional informationfor implementing or emulating a sensor 302 in hardware is described inFIG. 5.

Nodes 304 are configured to receive a correlithm object 104 (e.g. aninput correlithm object 104), to determine another correlithm object 104based on the received correlithm object 104, and to output theidentified correlithm object 104 (e.g. an output correlithm object 104).In one embodiment, nodes 304 are configured to use node tables 200 thatlink a plurality of correlithm objects 104 from a first n-dimensionalspace 102 with a plurality of correlithm objects 104 in a secondn-dimensional space 102. A node table 200 may be configured similar tothe table 200 described in FIG. 2. Additional information forimplementing or emulating a node 304 in hardware is described in FIG. 5.

Actors 306 serve as interfaces that allow a user device 100 to convertcorrelithm objects 104 in the correlithm object domain back to realworld values or data samples. Actors 306 enable the user device 100 toconvert from correlithm objects 104 into any suitable type of real worldvalue. Actors 306 are configured to receive a correlithm object 104(e.g. an output correlithm object 104), to determine a real world outputvalue 326 based on the received correlithm object 104, and to output thereal world output value 326. The real world output value 326 may be adifferent data type or representation of the original data sample. As anexample, the real world input value 320 may be an image 301 of a personand the resulting real world output value 326 may be text 327 and/or anaudio signal identifying the person. In one embodiment, actors 306 areconfigured to use actor tables 310 that link a plurality of correlithmobjects 104 in an n-dimensional space 102 with a plurality of real worldvalues. As an example, an actor table 310 may be configured with a firstcolumn 316 that lists correlithm objects 104 as output correlithmobjects and a second column 318 that lists real world values. In otherexamples, actor tables 310 may be configured in any other suitablemanner or may be implemented using any other suitable data structure. Insome embodiments, one or more mapping functions may be employed totranslate between a correlithm object 104 in an n-dimensional space anda real world output value 326. Additional information for implementingor emulating an actor 306 in hardware is described in FIG. 5.

A correlithm object processing system 300 uses a combination of a sensortable 308, a node table 200, and/or an actor table 310 to provide aspecific set of rules that improve computer-related technologies byenabling devices to compare and to determine the degree of similaritybetween different data samples regardless of the data type and/or formatof the data sample they represent. The ability to directly compare datasamples having different data types and/or formatting is a newfunctionality that cannot be performed using conventional computingsystems and data structures. Conventional systems require data samplesto be of the same type and/or format in order to perform any kind ofoperation on the data samples. In some instances, some types of datasamples are incompatible with each other and cannot be compared becausethere is no common format available. For example, conventional computersare unable to compare data samples of images with data samples of audiosamples because there is no common format available. In contrast, adevice implementing a correlithm object processing system uses acombination of a sensor table 308, a node table 200, and/or an actortable 310 to compare and perform operations using correlithm objects 104in the correlithm object domain regardless of the type or format of theoriginal data samples. The correlithm object processing system 300 usesa combination of a sensor table 308, a node table 200, and/or an actortable 310 as a specific set of rules that provides a particular solutionto dealing with different types of data samples and allows devices toperform operations on different types of data samples using correlithmobjects 104 in the correlithm object domain. In some instances,comparing data samples as correlithm objects 104 is computationally moreefficient and faster than comparing data samples in their originalformat. Thus, using correlithm objects 104 to represent data samplesprovides increased flexibility and improved performance compared tousing other conventional data structures. The specific set of rules usedby the correlithm object processing system 300 go beyond simply usingroutine and conventional activities in order to achieve this newfunctionality and performance improvements.

In addition, correlithm object processing system 300 uses a combinationof a sensor table 308, a node table 200, and/or an actor table 310 toprovide a particular manner for transforming data samples betweenordinal number representations and correlithm objects 104 in acorrelithm object domain. For example, the correlithm object processingsystem 300 may be configured to transform a representation of a datasample into a correlithm object 104, to perform various operations usingthe correlithm object 104 in the correlithm object domain, and totransform a resulting correlithm object 104 into another representationof a data sample. Transforming data samples between ordinal numberrepresentations and correlithm objects 104 involves fundamentallychanging the data type of data samples between an ordinal number systemand a categorical number system to achieve the previously describedbenefits of the correlithm object processing system 300.

FIG. 4 is a protocol diagram of an embodiment of a correlithm objectprocess flow 400. A user device 100 implements process flow 400 toemulate a correlithm object processing system 300 to perform operationsusing correlithm object 104 such as facial recognition. The user device100 implements process flow 400 to compare different data samples (e.g.images, voice signals, or text) to each other and to identify otherobjects based on the comparison. Process flow 400 provides instructionsthat allows user devices 100 to achieve the improved technical benefitsof a correlithm object processing system 300.

Conventional systems are configured to use ordinal numbers foridentifying different data samples. Ordinal based number systems onlyprovide information about the sequence order of numbers based on theirnumeric values, and do not provide any information about any other typesof relationships for the data samples being represented by the numericvalues such as similarity. In contrast, a user device 100 can implementor emulate the correlithm object processing system 300 which provides anunconventional solution that uses categorical numbers and correlithmobjects 104 to represent data samples. For example, the system 300 maybe configured to use binary integers as categorical numbers to generatecorrelithm objects 104 which enables the user device 100 to performoperations directly based on similarities between different datasamples. Categorical numbers provide information about how similardifferent data sample are from each other. Correlithm objects 104generated using categorical numbers can be used directly by the system300 for determining how similar different data samples are from eachother without relying on exact matches, having a common data type orformat, or conventional signal processing techniques.

A non-limiting example is provided to illustrate how the user device 100implements process flow 400 to emulate a correlithm object processingsystem 300 to perform facial recognition on an image to determine theidentity of the person in the image. In other examples, the user device100 may implement process flow 400 to emulate a correlithm objectprocessing system 300 to perform voice recognition, text recognition, orany other operation that compares different objects.

At step 402, a sensor 302 receives an input signal representing a datasample. For example, the sensor 302 receives an image of person's faceas a real world input value 320. The input signal may be in any suitabledata type or format. In one embodiment, the sensor 302 may obtain theinput signal in real-time from a peripheral device (e.g. a camera). Inanother embodiment, the sensor 302 may obtain the input signal from amemory or database.

At step 404, the sensor 302 identifies a real world value entry in asensor table 308 based on the input signal. In one embodiment, thesystem 300 identifies a real world value entry in the sensor table 308that matches the input signal. For example, the real world value entriesmay comprise previously stored images. The sensor 302 may compare thereceived image to the previously stored images to identify a real worldvalue entry that matches the received image. In one embodiment, when thesensor 302 does not find an exact match, the sensor 302 finds a realworld value entry that closest matches the received image.

At step 406, the sensor 302 identifies and fetches an input correlithmobject 104 in the sensor table 308 linked with the real world valueentry. At step 408, the sensor 302 sends the identified input correlithmobject 104 to the node 304. In one embodiment, the identified inputcorrelithm object 104 is represented in the sensor table 308 using acategorical binary integer string. The sensor 302 sends the binarystring representing to the identified input correlithm object 104 to thenode 304.

At step 410, the node 304 receives the input correlithm object 104 anddetermines distances 106 between the input correlithm object 104 andeach source correlithm object 104 in a node table 200. In oneembodiment, the distance 106 between two correlithm objects 104 can bedetermined based on the differences between the bits of the twocorrelithm objects 104. In other words, the distance 106 between twocorrelithm objects can be determined based on how many individual bitsdiffer between a pair of correlithm objects 104. The distance 106between two correlithm objects 104 can be computed using Hammingdistance or any other suitable technique. In another embodiment, thedistance 106 between two correlithm objects 104 can be determined usinga Minkowski distance such as the Euclidean or “straight-line” distancebetween the correlithm objects 104. For example, the distance 106between a pair of correlithm objects 104 may be determined bycalculating the square root of the sum of squares of the coordinatedifference in each dimension.

At step 412, the node 304 identifies a source correlithm object 104 fromthe node table 200 with the shortest distance 106. A source correlithmobject 104 with the shortest distance from the input correlithm object104 is a correlithm object 104 either matches or most closely matchesthe received input correlithm object 104.

At step 414, the node 304 identifies and fetches a target correlithmobject 104 in the node table 200 linked with the source correlithmobject 104. At step 416, the node 304 outputs the identified targetcorrelithm object 104 to the actor 306. In this example, the identifiedtarget correlithm object 104 is represented in the node table 200 usinga categorical binary integer string. The node 304 sends the binarystring representing to the identified target correlithm object 104 tothe actor 306.

At step 418, the actor 306 receives the target correlithm object 104 anddetermines distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310. The actor 306 maycompute the distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310 using a processsimilar to the process described in step 410.

At step 420, the actor 306 identifies an output correlithm object 104from the actor table 310 with the shortest distance 106. An outputcorrelithm object 104 with the shortest distance from the targetcorrelithm object 104 is a correlithm object 104 either matches or mostclosely matches the received target correlithm object 104.

At step 422, the actor 306 identifies and fetches a real world outputvalue in the actor table 310 linked with the output correlithm object104. The real world output value may be any suitable type of data samplethat corresponds with the original input signal. For example, the realworld output value may be text that indicates the name of the person inthe image or some other identifier associated with the person in theimage. As another example, the real world output value may be an audiosignal or sample of the name of the person in the image. In otherexamples, the real world output value may be any other suitable realworld signal or value that corresponds with the original input signal.The real world output value may be in any suitable data type or format.

At step 424, the actor 306 outputs the identified real world outputvalue. In one embodiment, the actor 306 may output the real world outputvalue in real-time to a peripheral device (e.g. a display or a speaker).In one embodiment, the actor 306 may output the real world output valueto a memory or database. In one embodiment, the real world output valueis sent to another sensor 302. For example, the real world output valuemay be sent to another sensor 302 as an input for another process.

FIG. 5 is a schematic diagram of an embodiment of a computerarchitecture 500 for emulating a correlithm object processing system 300in a user device 100. The computer architecture 500 comprises aprocessor 502, a memory 504, a network interface 506, and aninput-output (I/O) interface 508. The computer architecture 500 may beconfigured as shown or in any other suitable configuration.

The processor 502 comprises one or more processors operably coupled tothe memory 504. The processor 502 is any electronic circuitry including,but not limited to, state machines, one or more central processing unit(CPU) chips, logic units, cores (e.g. a multi-core processor),field-programmable gate array (FPGAs), application specific integratedcircuits (ASICs), graphics processing units (GPUs), or digital signalprocessors (DSPs). The processor 502 may be a programmable logic device,a microcontroller, a microprocessor, or any suitable combination of thepreceding. The processor 502 is communicatively coupled to and in signalcommunication with the memory 204. The one or more processors areconfigured to process data and may be implemented in hardware orsoftware. For example, the processor 502 may be 8-bit, 16-bit, 32-bit,64-bit or of any other suitable architecture. The processor 502 mayinclude an arithmetic logic unit (ALU) for performing arithmetic andlogic operations, processor registers that supply operands to the ALUand store the results of ALU operations, and a control unit that fetchesinstructions from memory and executes them by directing the coordinatedoperations of the ALU, registers and other components.

The one or more processors are configured to implement variousinstructions. For example, the one or more processors are configured toexecute instructions to implement sensor engines 510, node engines 512,trainer engines 513, and actor engines 514. In an embodiment, the sensorengines 510, the node engines 512, the trainer engines 513, and theactor engines 514 are implemented using logic units, FPGAs, ASICs, DSPs,or any other suitable hardware. The sensor engines 510, the node engines512, the trainer engines 513, and the actor engines 514 are eachconfigured to implement a specific set of rules or process that providesan improved technological result.

In one embodiment, the sensor engine 510 is configured to receive a realworld value 320 as an input, to determine a correlithm object 104 basedon the real world value 320, and to output the correlithm object 104.Examples of the sensor engine 510 in operation are described in FIGS. 4,6, 7, and 8.

In one embodiment, the node engine 512 is configured to receive acorrelithm object 104 (e.g. an input correlithm object 104), todetermine another correlithm object 104 based on the received correlithmobject 104, and to output the identified correlithm object 104 (e.g. anoutput correlithm object 104). The node engine 512 is also configured tocompute distances between pairs of correlithm objects 104. Examples ofthe node engine 512 in operation are described in FIGS. 4, 6, 7, and 8.

In one embodiment, the trainer engine 513 implements a trainerconfigured to facilitate generating new entries in sensor tables 308,node tables 200, and actor tables 310 for a correlithm object processingsystem. The trainer engine 513 is configured to receive an input signalcomprising information for new entries to be generated. The trainerengine 513 is configured to exchange information between a sensor 302, anode 304, and/or an actor 306 to generate entries in sensor tables 308,node tables 200, and actor tables 310, respectively. By generatingentries in sensor tables 308, node tables 200, and actor tables 310, thetrainer engine 513 is able to extend the functionality of a correlithmobject processing system. In other words, the trainer engine 513 helpsthe correlithm object processing system grow by allowing the correlithmobject processing system to handle new inputs and outputs. In oneembodiment, when a correlithm object processing system receives an input(e.g. a real world input or a correlithm object) that is not linked withany entries in a sensor table 308, a node table 200, and/or an actortable 310, the correlithm object processing system may be unable toprocess or perform any operation based on the input. The trainer engine513 trains the correlithm object processing system by modifying a sensortable 308, a node table 200, and/or an actor table 310 to handle any newinputs or outputs for the correlithm object processing system. Examplesof the trainer engine 513 in operation are described in FIGS. 6-8.

In one embodiment, the actor engine 514 is configured to receive acorrelithm object 104 (e.g. an output correlithm object 104), todetermine a real world output value 326 based on the received correlithmobject 104, and to output the real world output value 326. Examples ofthe actor engine 514 in operation are described in FIGS. 4, 6, 7, and 8.

The memory 504 comprises one or more non-transitory disks, tape drives,or solid-state drives, and may be used as an over-flow data storagedevice, to store programs when such programs are selected for execution,and to store instructions and data that are read during programexecution. The memory 504 may be volatile or non-volatile and maycomprise read-only memory (ROM), random-access memory (RAM), ternarycontent-addressable memory (TCAM), dynamic random-access memory (DRAM),and static random-access memory (SRAM). The memory 504 is operable tostore sensor instructions 516, node instructions 518, trainerinstructions 523, actor instructions 520, sensor tables 308, node tables200, actor tables 310, and/or any other data or instructions. The sensorinstructions 516, the node instructions 518, trainer instructions 523,and the actor instructions 520 comprise any suitable set ofinstructions, logic, rules, or code operable to execute a sensor engine510, a node engine 512, a trainer engine 513, and a actor engine 514,respectively.

The sensor tables 308, the node tables 200, and the actor tables 310 maybe configured similar to the sensor tables 308, the node tables 200, andthe actor tables 310 described in FIG. 3, respectively.

The network interface 506 is configured to enable wired and/or wirelesscommunications. The network interface 506 is configured to communicatedata with any other device or system. For example, the network interface506 may be configured for communication with a modem, a switch, arouter, a bridge, a server, or a client. The processor 502 is configuredto send and receive data using the network interface 506.

The I/O interface 508 may comprise ports, transmitters, receivers,transceivers, or any other devices for transmitting and/or receivingdata with peripheral devices as would be appreciated by one of ordinaryskill in the art upon viewing this disclosure. For example, the I/Ointerface 508 may be configured to communicate data between theprocessor 502 and peripheral hardware such as a graphical userinterface, a display, a mouse, a keyboard, a key pad, and a touch sensor(e.g. a touch screen).

FIGS. 6-8 generally describe an embodiment for training a correlithmobject processing system. In FIG. 6-8, the correlithm object processingsystem comprises a trainer 602 configured to facilitate generating newentries in sensor tables 308, node tables 200, and actor tables 310 fora correlithm object processing system. The correlithm object processingsystem training process may be implemented to extend the functionalityof the correlithm object processing system, for example to handle newreal world input values and/or real world output values.

A trainer 602 is generally configured to train the correlithm objectprocessing system 600 by facilitating the generation of new entries in asensor table 308, a node table 200, and/or an actor table 310. Forexample, the correlithm object processing system 600 may receive a realword input value 604 that is not linked with any correlithm objects 104in the correlithm object processing system. In one embodiment, thecorrelithm object processing system 600 may be unable to process thereal world input value 604 without linking it to a correlithm object104. The trainer 602 is configured to train the correlithm objectprocessing 600 for processing the real world input value 604 bygenerating new entries in a sensor table 308, a node table 200, and/oran actor table 310. By generating new entries, the trainer 602 increasesthe functionality of the correlithm object processing system 600 and itsability to handle and process new real world input values 604 and/orreal world output values 606. Trainer 602 may be configured to generateentries in individual tables (e.g. a sensor table 308, a node table 200,or an actor table 310) or in a set of tables along an end-to-end path,for example from a sensor 302 to an actor 306.

FIG. 6 is a schematic diagram of an embodiment of a correlithm objectprocessing system 600 that comprises a trainer 602, a sensor 302, a node304, and an actor 306. In other embodiments, the correlithm objectprocessing system 600 may comprise any other suitable type and/or numberof components. The components in the correlithm object processing system600 may be configured as shown or in any other suitable configuration.In FIG. 6, the sensor 302 is configured to receive a real world inputvalue 604 and to output a correlithm object 104 to the node 304 based onthe sensor table 308. The node 304 is configured to receive thecorrelithm object 104 from the sensor 302 and to output anothercorrelithm object 104 to the actor 306 based on the node table 200. Theactor 306 is configured to receive the correlithm object 104 from thenode 304 and to output a real world output value 306 based on the actortable 310. The sensor 302, node 304, and actor 306 may be configured tooperate similar to the operation described in FIG. 3.

The trainer 602 is in signal communication with the components in thecorrelithm object processing system 600. The trainer 602 is configuredto use any suitable type of the signal channels to send commands orinstructions 603 to the components the correlithm object processingsystem 600. The signal channels may be any suitable type of channel ormechanism for communicating commands 603 to the components in thecorrelithm object processing system 600. The trainer 602 may beconfigured to send commands 603 to individual components or multiplecomponents at once. In some embodiments, the trainer 602 may beconfigured to access a sensor table 308, a node table 200, or an actortable 310 directly to create entries in the table. For example, thetrainer 602 may be configured to access a node table 200 directly andcreate an entry in the node table 200 that links a source correlithmobject with a target correlithm object. As another example, the trainer602 may be configured to access a sensor table 308 directly and tocreate an entry in the sensor table 308 that links a real world inputvalue 604 with a correlithm object 104. As another example, the trainer602 may be configured to access an actor table 310 directly and tocreate an entry in the actor table 310 that links a correlithm object104 with a real world output value 606.

The trainer 602 is configured to receive an input signal 605. The inputsignal 605 may be sent by a processor (e.g. processor 502), acontroller, an operator, a programmer, a sensor 302, a node 304, anactor 306, or any other suitable source. The input signal 605 is atrigger signal that initiates a correlithm object processing systemtraining process by the trainer 602. Examples of the input signal 605include, but are not limited to, a function call or a data message. Theinput signal 605 may identify one or more real world input values, realworld output values, and/or correlithm objects 104. For example, theinput signal 605 may identify a real world input value 604 and a realworld output 606 to be inserted into a correlithm object processingsystem. As another example, the input signal 605 may also identifyparticular correlithm objects 104 that are to be linked with the realworld input value 604 and/or the real world output 606. The trainer 602is configured to facilitate the generation of entries in a sensor table308, a node table 200, and/or an actor table 310 in response toreceiving an input signal 605. In one embodiment, the informationprovided by the input signal 605 may be used to indicate a particulartype of correlithm object processing system training method. Forexample, the input signal 605 may include a flag or value that indicateswhich type of correlithm object processing system training method touse. Examples of different correlithm object processing system trainingmethods are described in FIGS. 7 and 8.

FIG. 7 is a protocol diagram of an embodiment of a correlithm objectprocessing system training flow 700. Process flow 700 modifies acorrelithm object processing system to support a new set of real worldinput values and real world output values. A user device 100 mayimplement process flow 700 to train the correlithm object processingsystem by generating new entries in a sensor table 308, node table 200,and/or an actor table 310. By generating new entries, the trainer 602increases the functionality of the correlithm object processing system600 and its ability to handle and process new real world input values604 and/or real world output values 606. Process flow 700 works by firstgenerating entries in a source table 308 and an actor table 310 for aset of real world input values 604 and a real world output values 606,respectively. Process flow 700 then generates an entry in a node table200 that links correlithm objects 104 for the real world input value 604and the real world output value 606.

At step 702, the trainer 602 receives a real world input value 604 and areal world output value 606. The real world input value 604 and the realworld output value 606 may be text, numbers, images, audio signals, orany other suitable type of real world values or representations. In oneembodiment, the trainer 602 receives the real world input value 604 andthe real world value 606 as a request from an operator to add the realworld input value 604 and the real world output value 606 to thecorrelithm object processing system 600. In this example, the trainer602 modifies sensors 302, nodes 304, and/or actors 306 in the correlithmobject processing system to enable the correlithm object processingsystem to support and process the new set of new real world input values604 and real world output values 606.

In another embodiment, the trainer 602 receives the real world inputvalue 604 and the real world value 606 after a determination thatcorrelithm objects 104 linked with the real world input value 604 or thereal world output value 606 are not present in a node table 200. Forexample, a node 304 may receive an input correlithm object 104 linkedwith the real world input value 604 and may compare the input correlithmobject 104 to the source correlithm objects in its node table 200 todetermine distances (e.g. hamming distances) between the inputcorrelithm object 104 and the source correlithm objects 104. The node304 may determine distances using any of the previously describedtechniques. The node 304 determines whether the input correlithm object104 is present in the node table 200 based on the distances between theinput correlithm object 104 and the source correlithm objects 104. Forexample, the node 304 may determine the input correlithm object 104 isnot present in the node table 200 when the input correlithm object 104does not match (i.e. have a distance of zero) any of the sourcecorrelithm objects 104 in the node table 200. As another example, thenode 304 may determine the input correlithm object 104 is not present inthe node table 200 when the distances between input correlithm object104 and the source correlithm objects 104 exceed a core distancethreshold. The core distance threshold defines how different correlithmobjects can be from each other to still be considered a match. Forinstance, a core distance threshold may be set to 10 bits which meansthat the input correlithm object 104 and a source correlithm object 104are considered a match as long as they have no more than 10 bitsdifferent from each other. The core distance threshold may be set to anysuitable value. The input correlithm object 104 is considered a matchwith a source correlithm object 104 when the distance between the inputcorrelithm object 104 and a source correlithm object 104 is equal to orless than the core distance.

In another embodiment, the trainer 602 receives the real world inputvalue 604 and the real world output value 606 after a determination thatthe real world input value 604 and/or the real world output value 606are not present in a sensor table 308 or an actor table 310,respectively. In these examples, the real world input value 604 and/orthe real world output value 606 are not currently supported by thecorrelithm object processing system. In either of these cases, thetrainer 602 receives the real world input value 604 and/or the realworld output value 606 as a request to modify the correlithm objectprocessing system to support the real world input value 604 and the realworld output value 606.

At step 704, the trainer 602 sends the real world input value 604 to thesensor 302 and, at step 706, receives a source correlithm object 104from the sensor 302 in response to sending the real world input value604. For example, the trainer 602 may provide the real world input value604 to the sensor 302 as a request for the sensor 302 to generate orallocate a source correlithm object 104 based on the real world inputvalue 604. The sensor 302 may generate the source correlithm object 104by identifying an unused or unmapped correlithm object 104 in then-dimensional space 102 where the other source correlithm objects 104 inthe sensor table 308 are located. The sensor 302 then assigns theidentified correlithm object 104 as the source correlithm object 104. Asanother example, the sensor 302 may apply one or more operations to thereal world input value 604 to generate the source correlithm object 104.In other examples, the sensor 302 may generate the source correlithmobject 104 using any other suitable technique. In one embodiment, thesensor 302 stores the relationship between the real world input value604 and the source correlithm object in its sensor table 308 bygenerating an entry in the sensor table 308 that links the real worldinput value 604 with the source correlithm object 104.

At step 708, the trainer 602 sends the real world output value 606 tothe actor 306, and at step 710, receives a target correlithm object 104from the actor 306 in response to sending the real world out value 606.For example, the trainer 602 may provide the real world output value 606to the actor 306 as a request for the actor 306 to generate a targetcorrelithm object 104 based on the real world output value 606. Theactor 306 may generate or allocate the target correlithm object 104 byidentifying an unused or unmapped correlithm object 104 in then-dimensional space 102 where the other target correlithm objects 104 inthe actor table 310 are located. The actor 306 then assigns theidentified correlithm object 104 as the target correlithm object 104. Asanother example, the actor 306 may apply one or more operations to thereal world output value 606 to generate the target correlithm object104. In this example, the actor 306 is configured to operate in areverse direction to generate the target correlithm object 104 based onthe real world output value 606. In other examples, the actor 306 maygenerate the target correlithm object 104 using any other suitabletechnique. In one embodiment, the actor 306 stores the relationshipbetween the real world output value 606 and the target correlithm object104 in its actor table 310 by generating an entry in the actor table 310that links the real world output value 606 with the target correlithmobject 104.

At step 712, the trainer 602 sends the source correlithm object 104 andthe target correlithm object 104 to the node 304. The trainer 602 mayprovide the source correlithm object 104 and the target correlithmobject 104 to the node 304 as a request to generate an entry in its nodetable 200. At step 714, the node 304 creates an entry in the node table200 linking the source correlithm 104 with the target correlithm object104. In another embodiment, the trainer 602 accesses the node table 200directly and creates an entry in the node table 200 that links thesource correlithm object 104 with the target correlithm object 104. Oncethe sensor table 308, the node table 200, and the actor table 310 havebeen updated, the correlithm object processing system 600 is then ableto handle and process the new real world input values 604 and real worldoutput values 606.

FIG. 8 is a protocol diagram of another embodiment of a correlithmobject processing system training flow 800. Similar to the process flow700 described in FIG. 7, a user device 100 implements process flow 800to train a correlithm object processing system by generating new entriesin a sensor table 308, node table 200, and/or an actor table 310.Process flow 800 works in the reverse order of process flow 700 by firstallocating an entry in a node table 200 with a pair of correlithmobjects 104 and then linking a real world input value 604 and a realworld output value 606 to the correlithm objects 104 in a sensor table308 and an actor table 310, respectively.

At step 802, the trainer 602 receives a real world input value 604 and areal world output value 606. The trainer 602 may receive the real worldinput value 604 and the real world output value 606 as part of a processsimilar to the process described in step 702 of FIG. 7.

At step 804, the trainer 602 sends a node entry request to the node 304in response to receiving the real world input value 604 and the realworld output value 606. The trainer 602 sends the node entry request totrigger the node 304 to generate or allocate an entry in the node table200 that will be linked with the real world input value 604 and the realworld output value 606. The node entry request may be any suitable typeof signal or message that triggers the node 304 to generate or allocatean entry in the node table 200. At step 806, the node 304 creates anentry in a node table 200 linking a source correlithm object 104 with atarget correlithm object 104. In one embodiment, the node 304 maygenerate the source correlithm object 104 and the target correlithmobject 104 by identifying unused or unmapped correlithm objects 104 inthe n-dimensional space 102 where the other source correlithm objects104 and target correlithm objects 104 are located. The node 304 thenassigns the correlithm objects 104 as the source correlithm object 104and the target correlithm object 104. In other examples, the node 304may generate the source correlithm object 104 and the target correlithmobject 104 using any other suitable technique. In one embodiment, thenode 304 stores the relationship between the source correlithm object106 and the target correlithm object 106 in its node table 200 bygenerating an entry in the node table 200 that links the sourcecorrelithm object 104 with the target correlithm object 104. The node304 sends the source correlithm object 104 and the target correlithm 104to the trainer 602 once they have been to the node table 200.

At step 808, the trainer 602 receives the source correlithm object 104and the target correlithm object 104 from the node 304 in response tosending the node entry request. At step 810, the trainer 602 sends thereal world input value 604 and the source correlithm object 104 to thesensor 302. The trainer 602 sends the real world input value 604 and thesource correlithm object 104 to the sensor 302 as a request for thesensor 302 to add the real world input value 604 and the sourcecorrelithm object 104 to its sensor table 308. At step 812, the sensor302 creates an entry in the sensor table 308 linking the real worldinput value 604 and the source correlithm object 104. The sensor 302stores the relationship between the real world input value 604 and thesource correlithm object 104 in its sensor table 308 by generating a newentry in the sensor table 308 that links the real world input value 604with the source correlithm object 104.

At step 814, the trainer 602 sends the real world output value 606 andthe target correlithm object 104 to the actor 306. The trainer 602 maysend the real world output value 606 and the target correlithm object104 to the actor 306 as a request for the actor 306 to add the realworld output value 606 and the target correlithm object 104 to its actortable 310. At step 816, the actor 306 creates an entry in the actortable 310 linking the real world output value 606 and the targetcorrelithm object 104. The actor 306 stores the relationship between thereal world output value 606 and the target correlithm object 104 in itsactor table 310 by generating a new entry in the actor table 310 thatlinks the real world output value 606 with the target correlithm object104. Similar to process flow 700, once the sensor table 308, the nodetable 200, and the actor table 310 have been updated, the correlithmobject processing system 600 is then able to handle and process the newreal world input values 604 and real world output values 606.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated in another systemor certain features may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as coupled or directly coupled orcommunicating with each other may be indirectly coupled or communicatingthrough some interface, device, or intermediate component whetherelectrically, mechanically, or otherwise. Other examples of changes,substitutions, and alterations are ascertainable by one skilled in theart and could be made without departing from the spirit and scopedisclosed herein.

To aid the Patent Office, and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants notethat they do not intend any of the appended claims to invoke 35 U.S.C. §112(f) as it exists on the date of filing hereof unless the words “meansfor” or “step for” are explicitly used in the particular claim.

1. A system configured to train a correlithm object processing system,comprising: a node linked with a node table that identifies: a pluralityof source correlithm objects, wherein each source correlithm object is apoint in an n-dimensional space represented by a binary string; and aplurality of target correlithm objects, wherein: each target correlithmobject is a point in the n-dimensional space represented by a binarystring, and each target correlithm object is linked with a sourcecorrelithm object from among the plurality of source correlithm objects;and a trainer operably coupled to the memory, configured to: receive areal world input value and a real world output value; send a node entryrequest to the node in response to receiving the real world input valueand the real world output value; receive a source correlithm object anda target correlithm object in response to sending the node entryrequest; send the real world input value and the source correlithmobject to a sensor; send the real world output value and the targetcorrelithm object to an actor; the sensor operably coupled to thetrainer, configured to: receive the real world input value and thesource correlithm object; generate an entry in a sensor table linkingthe real world input value and the source correlithm object; and theactor operably coupled to the trainer, configured to: receive the realworld output value and the target correlithm object; generate an entryin an actor table linking the real world output value and the targetcorrelithm object.
 2. The system of claim 1, wherein the node isconfigured to: receive an input correlithm object linked with the realworld input value; compare the input correlithm object to the sourcecorrelithm objects in the node table; and determine the input correlithmobject does not match any of the source correlithm objects; and whereinthe trainer receives the real world input value and the real worldoutput value after the node determines that the input correlithm objectdoes not match any of the source correlithm objects.
 3. The system ofclaim 1, wherein the node is configured to: receive an input correlithmobject linked with the real world input value; determine distancesbetween the input correlithm object and each of the source correlithmobjects in the node table, wherein the distance between the inputcorrelithm object and a source correlithm object is based on thedifferences between a binary string representing the input correlithmobject and binary strings linked with each of the source correlithmobjects; and determine none of the distances are within a core distancethreshold; and wherein the trainer receives the real world input valueand the real world output value after the node determines that none ofthe distances are within the core distance threshold.
 4. The system ofclaim 3, wherein determining distances between the input correlithmobject and each of the source correlithm objects comprises determining ahamming distance between the input correlithm object and a sourcecorrelithm object.
 5. The system of claim 3, wherein determiningdistances between the input correlithm object and each of the sourcecorrelithm objects comprises: performing an XOR operation between theinput correlithm object and a source correlithm object to generate abinary string; and counting the number of logical high values in thebinary string, wherein the number of logical high represents a distance.6. The system of claim 1, wherein the sensor is linked with a sensortable comprising: a plurality of correlithm objects; a plurality of realworld input values; and wherein the sensor table links each correlithmobject from the plurality of correlithm objects with a real world inputvalue from the plurality of real world input values.
 7. The system ofclaim 1, wherein the actor is linked with an actor table comprising: aplurality of correlithm objects; a plurality of real world outputvalues; and wherein the actor table links each correlithm object fromthe plurality of correlithm objects with a real world output value fromthe plurality of real world output values.
 8. A correlithm objectprocessing system training method, comprising: receiving, by a trainerengine, a real world input value and a real world output value; sending,by the trainer engine, a node entry request to a node engine in responseto receiving the real world input value and the real world output value,wherein the node entry request triggers the node engine to generate anentry in a node table that identifies: a plurality of source correlithmobjects, wherein each source correlithm object is a point in ann-dimensional space represented by a binary string; and a plurality oftarget correlithm objects, wherein: each target correlithm object is apoint in the n-dimensional space represented by a binary string, andeach target correlithm object is linked with a source correlithm objectfrom among the plurality of source correlithm objects; receiving, by thetrainer engine, a source correlithm object and a target correlithmobject in response to sending the node entry request; sending, by thetrainer engine, the real world input value and the source correlithmobject to a sensor engine; generating, by the sensor engine, an entry ina sensor table linking the real world input value and the sourcecorrelithm object in response to receiving the real world input valueand the source correlithm object; sending, by the trainer engine, thereal world output value and the target correlithm object to an actorengine; and generating, by the actor engine, an entry in an actor tablelinking the real world output value and the target correlithm object inresponse to receiving the real world output value and the targetcorrelithm object.
 9. The method of claim 8, further comprising:receiving, by a node engine, an input correlithm object linked with thereal world input value; comparing, by the node engine, the inputcorrelithm object to the source correlithm objects in the node table;and determining, by the node engine, the input correlithm object doesnot match any of the source correlithm objects; and wherein the trainerengine receives the real world input value and the real world outputvalue after the node engine determines that the input correlithm objectdoes not match any of the source correlithm objects.
 10. The method ofclaim 8, further comprising: receiving, by a node engine, an inputcorrelithm object linked with the real world input value; determining,by the node engine, distances between the input correlithm object andeach of the source correlithm objects in the node table, wherein thedistance between the input correlithm object and a source correlithmobject is based on the differences between a binary string representingthe input correlithm object and binary strings linked with each of thesource correlithm objects; and determining, by the node engine, none ofthe distances are within a core distance threshold; and wherein thetrainer engine receives the real world input value and the real worldoutput value after the node engine determines that none of the distancesare within the core distance threshold.
 11. The method of claim 10,wherein determining distances between the input correlithm object andeach of the source correlithm objects comprises determining a hammingdistance between the input correlithm object and a source correlithmobject.
 12. The method of claim 10, wherein determining distancesbetween the input correlithm object and each of the source correlithmobjects comprises: performing an XOR operation between the inputcorrelithm object and a source correlithm object to generate a binarystring; and counting the number of logical high values in the binarystring, wherein the number of logical high represents a distance. 13.The method of claim 8, wherein the sensor engine is linked with a sensortable comprising: a plurality of correlithm objects; a plurality of realworld input values; and wherein the sensor table links each correlithmobject from the plurality of correlithm objects with a real world inputvalue from the plurality of real world input values.
 14. The method ofclaim 8, wherein the actor engine is linked with an actor tablecomprising: a plurality of correlithm objects; a plurality of real worldoutput values; and wherein the actor table links each correlithm objectfrom the plurality of correlithm objects with a real world output valuefrom the plurality of real world output values.
 15. A computer programproduct comprising executable instructions stored in a non-transitorycomputer readable medium such that when executed by a processor causesthe processor to emulate a trainer in a correlithm object processingsystem configured to: receive a real world input value and a real worldoutput value; send a node entry request to a node engine in response toreceiving the real world input value and the real world output value,wherein the node entry request triggers the node engine to generate anentry in a node table that identifies: a plurality of source correlithmobjects, wherein each source correlithm object is a point in ann-dimensional space represented by a binary string; and a plurality oftarget correlithm objects, wherein: each target correlithm object is apoint in the n-dimensional space represented by a binary string, andeach target correlithm object is linked with a source correlithm objectfrom among the plurality of source correlithm objects; receive a sourcecorrelithm object and a target correlithm object in response to sendingthe node entry request; send the real world input value and the sourcecorrelithm object to a sensor engine, wherein sending the real worldinput value and the source correlithm object to the sensor enginetriggers the sensor engine to generate an entry in a sensor tablelinking the real world input value and the source correlithm object;send the real world output value and the target correlithm object to anactor engine, wherein sending the real world output value and the targetcorrelithm object to the actor engine triggers the actor engine togenerate an entry in an actor table linking the real world output valueand the target correlithm object.
 16. The computer program product ofclaim 15, wherein the trainer receives the real world input value andthe real world output value after the node engine determines that aninput correlithm object linked with the real world input value does notmatch any of the source correlithm objects in the node table.
 17. Thecomputer program product of claim 15, wherein the trainer receives thereal world input value and the real world output value after the nodeengine determines that an input correlithm object linked with the realworld input value is not within a core distance threshold from any ofthe source correlithm objects in the node table.
 18. The computerprogram product of claim 17, wherein determining distances between theinput correlithm object and each of the source correlithm objectscomprises determining a hamming distance between the input correlithmobject and a source correlithm object.
 19. The computer program productof claim 17, wherein determining distances between the input correlithmobject and each of the source correlithm objects comprises: performingan XOR operation between the input correlithm object and a sourcecorrelithm object to generate a binary string; and counting the numberof logical high values in the binary string, wherein the number oflogical high represents a distance.
 20. The computer program product ofclaim 15, wherein: the sensor engine is linked with a sensor tablecomprising: a plurality of correlithm objects; a plurality of real worldinput values; and wherein the sensor table links each correlithm objectfrom the plurality of correlithm objects with a real world input valuefrom the plurality of real world input values; and the actor engine islinked with an actor table comprising: a plurality of correlithmobjects; a plurality of real world output values; and wherein the actortable links each correlithm object from the plurality of correlithmobjects with a real world output value from the plurality of real worldoutput values.